After all the descriptors of each feature point are created, the best-bin-first (BBF) [1]} matching method is adopted to obtain the initial matches of the feature points, when the cosine similarity is used as the measurement. For multi-source images, there is likely to be a large number of mismatches in initial matches, which causes common outlier removal algorithms to fail, such as RANSAC (random sample consensus). In the proposed algorithm, the consistency of the main orientation and spatial distribution of the feature points [2]} are used for mismatch removal. This is because although there may be spatial distortion in the multi-source remote sensing image pairs, there will not be too much deformation on the whole. Then, assuming that there is only overall rotation or scaling between the image pair. So the angle between the main orientation of each pair of correctly matched feature points should be consistent, and their spatial relationship with other correctly matched points in the two images should also be consistent. Therefore, this method is suitable for multi-source remote sensing images.
